---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_deit_tiny_adamax_00001_fold2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8535773710482529
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_1x_deit_tiny_adamax_00001_fold2

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co./facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9040
- Accuracy: 0.8536

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7328        | 1.0   | 75   | 0.6585          | 0.7321   |
| 0.4509        | 2.0   | 150  | 0.4910          | 0.7903   |
| 0.3653        | 3.0   | 225  | 0.4211          | 0.8253   |
| 0.2986        | 4.0   | 300  | 0.4104          | 0.8353   |
| 0.2809        | 5.0   | 375  | 0.3830          | 0.8336   |
| 0.2519        | 6.0   | 450  | 0.3604          | 0.8502   |
| 0.2227        | 7.0   | 525  | 0.3681          | 0.8552   |
| 0.2223        | 8.0   | 600  | 0.3795          | 0.8419   |
| 0.1548        | 9.0   | 675  | 0.3730          | 0.8552   |
| 0.1857        | 10.0  | 750  | 0.3841          | 0.8602   |
| 0.1291        | 11.0  | 825  | 0.3934          | 0.8602   |
| 0.08          | 12.0  | 900  | 0.4226          | 0.8552   |
| 0.0831        | 13.0  | 975  | 0.4456          | 0.8486   |
| 0.0574        | 14.0  | 1050 | 0.5173          | 0.8436   |
| 0.0548        | 15.0  | 1125 | 0.4816          | 0.8602   |
| 0.051         | 16.0  | 1200 | 0.5112          | 0.8569   |
| 0.0349        | 17.0  | 1275 | 0.5235          | 0.8536   |
| 0.0192        | 18.0  | 1350 | 0.5709          | 0.8502   |
| 0.027         | 19.0  | 1425 | 0.6300          | 0.8453   |
| 0.0409        | 20.0  | 1500 | 0.6458          | 0.8502   |
| 0.009         | 21.0  | 1575 | 0.6679          | 0.8552   |
| 0.0172        | 22.0  | 1650 | 0.6845          | 0.8519   |
| 0.0015        | 23.0  | 1725 | 0.7310          | 0.8552   |
| 0.0059        | 24.0  | 1800 | 0.7388          | 0.8552   |
| 0.0018        | 25.0  | 1875 | 0.7514          | 0.8569   |
| 0.0008        | 26.0  | 1950 | 0.7646          | 0.8552   |
| 0.0024        | 27.0  | 2025 | 0.7898          | 0.8569   |
| 0.0185        | 28.0  | 2100 | 0.7969          | 0.8519   |
| 0.0012        | 29.0  | 2175 | 0.8175          | 0.8619   |
| 0.003         | 30.0  | 2250 | 0.8189          | 0.8536   |
| 0.0009        | 31.0  | 2325 | 0.8193          | 0.8569   |
| 0.0003        | 32.0  | 2400 | 0.8343          | 0.8602   |
| 0.0006        | 33.0  | 2475 | 0.8317          | 0.8586   |
| 0.0003        | 34.0  | 2550 | 0.8413          | 0.8536   |
| 0.026         | 35.0  | 2625 | 0.8594          | 0.8519   |
| 0.0003        | 36.0  | 2700 | 0.8747          | 0.8519   |
| 0.0002        | 37.0  | 2775 | 0.8582          | 0.8536   |
| 0.0003        | 38.0  | 2850 | 0.8927          | 0.8536   |
| 0.0067        | 39.0  | 2925 | 0.8896          | 0.8519   |
| 0.0002        | 40.0  | 3000 | 0.8915          | 0.8536   |
| 0.003         | 41.0  | 3075 | 0.8737          | 0.8586   |
| 0.0003        | 42.0  | 3150 | 0.9065          | 0.8519   |
| 0.0159        | 43.0  | 3225 | 0.8958          | 0.8552   |
| 0.0007        | 44.0  | 3300 | 0.8969          | 0.8519   |
| 0.0002        | 45.0  | 3375 | 0.9007          | 0.8519   |
| 0.0002        | 46.0  | 3450 | 0.9037          | 0.8536   |
| 0.007         | 47.0  | 3525 | 0.9095          | 0.8536   |
| 0.0002        | 48.0  | 3600 | 0.9035          | 0.8536   |
| 0.0057        | 49.0  | 3675 | 0.9034          | 0.8536   |
| 0.0097        | 50.0  | 3750 | 0.9040          | 0.8536   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0